Overview

Brought to you by YData

Dataset statistics

Number of variables26
Number of observations150
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory133.8 KiB
Average record size in memory913.6 B

Variable types

Text2
DateTime1
Categorical15
Numeric8

Alerts

cant_apercibimientos has constant value "0.0" Constant
cluster_k5 has constant value "3" Constant
anio_preinscripcion is highly overall correlated with antiguedad and 1 other fieldsHigh correlation
antiguedad is highly overall correlated with anio_preinscripcion and 1 other fieldsHigh correlation
cant_Apoderado is highly overall correlated with cant_MontoLimite and 3 other fieldsHigh correlation
cant_MontoLimite is highly overall correlated with cant_Apoderado and 1 other fieldsHigh correlation
cant_antecedentes is highly overall correlated with cant_procesos_adjudicadoHigh correlation
cant_autenticado is highly overall correlated with cant_Apoderado and 3 other fieldsHigh correlation
cant_noAutenticado is highly overall correlated with cant_Apoderado and 1 other fieldsHigh correlation
cant_procesos_adjudicado is highly overall correlated with cant_antecedentes and 2 other fieldsHigh correlation
cant_representante is highly overall correlated with cant_autenticado and 1 other fieldsHigh correlation
cant_sinMontoLimite is highly overall correlated with cant_Apoderado and 3 other fieldsHigh correlation
cant_suspensiones is highly overall correlated with cant_procesos_adjudicadoHigh correlation
dtotal_articulos_provee is highly overall correlated with total_articulos_proveeHigh correlation
monto_total_adjudicado is highly overall correlated with cant_procesos_adjudicadoHigh correlation
periodo_preinscripcion is highly overall correlated with anio_preinscripcion and 1 other fieldsHigh correlation
provincia is highly overall correlated with total_articulos_proveeHigh correlation
total_articulos_provee is highly overall correlated with dtotal_articulos_provee and 1 other fieldsHigh correlation
cant_suspensiones is highly imbalanced (94.2%) Imbalance
cant_antecedentes is highly imbalanced (94.2%) Imbalance
cant_MontoLimite is highly imbalanced (60.7%) Imbalance
CUIT has unique values Unique
Nombre has unique values Unique
monto_total_adjudicado has 2 (1.3%) zeros Zeros
antiguedad has 8 (5.3%) zeros Zeros
cant_socios has 8 (5.3%) zeros Zeros
cant_Apoderado has 41 (27.3%) zeros Zeros

Reproduction

Analysis started2025-07-08 14:19:34.639600
Analysis finished2025-07-08 14:19:40.676733
Duration6.04 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

CUIT
Text

Unique 

Distinct150
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size11.1 KiB
2025-07-08T11:19:40.801797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length13
Median length11
Mean length10.986667
Min length9

Characters and Unicode

Total characters1648
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique150 ?
Unique (%)100.0%

Sample

1st row30558766987
2nd row30715168800
3rd row30664177818
4th row30620477113
5th row30629086524
ValueCountFrequency (%)
30558766987 1
 
0.7%
30715168800 1
 
0.7%
30664177818 1
 
0.7%
30620477113 1
 
0.7%
30629086524 1
 
0.7%
33708630409 1
 
0.7%
30525366657 1
 
0.7%
30505725774 1
 
0.7%
30710910916 1
 
0.7%
30501778407 1
 
0.7%
Other values (140) 140
93.3%
2025-07-08T11:19:41.003101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 291
17.7%
3 265
16.1%
7 186
11.3%
1 163
9.9%
6 156
9.5%
8 132
8.0%
4 127
7.7%
2 116
 
7.0%
5 114
 
6.9%
9 95
 
5.8%
Other values (3) 3
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1648
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 291
17.7%
3 265
16.1%
7 186
11.3%
1 163
9.9%
6 156
9.5%
8 132
8.0%
4 127
7.7%
2 116
 
7.0%
5 114
 
6.9%
9 95
 
5.8%
Other values (3) 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1648
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 291
17.7%
3 265
16.1%
7 186
11.3%
1 163
9.9%
6 156
9.5%
8 132
8.0%
4 127
7.7%
2 116
 
7.0%
5 114
 
6.9%
9 95
 
5.8%
Other values (3) 3
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1648
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 291
17.7%
3 265
16.1%
7 186
11.3%
1 163
9.9%
6 156
9.5%
8 132
8.0%
4 127
7.7%
2 116
 
7.0%
5 114
 
6.9%
9 95
 
5.8%
Other values (3) 3
 
0.2%

Nombre
Text

Unique 

Distinct150
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size13.1 KiB
2025-07-08T11:19:41.159260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length82
Median length43
Mean length20.533333
Min length6

Characters and Unicode

Total characters3080
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique150 ?
Unique (%)100.0%

Sample

1st rowEDITORIAL ALBATROS
2nd rowconstruir futuros
3rd rowFUNDACION DE LA UNIVERSIDAD NACIONAL DEL SUR
4th rowMULTICABLE S.A.
5th rowAD-HOC S.R.L.
ValueCountFrequency (%)
s.a 37
 
7.7%
srl 33
 
6.8%
s.r.l 27
 
5.6%
de 21
 
4.4%
sa 16
 
3.3%
la 9
 
1.9%
y 7
 
1.5%
argentina 6
 
1.2%
s.a.s 4
 
0.8%
cooperativa 4
 
0.8%
Other values (292) 318
66.0%
2025-07-08T11:19:41.416538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
332
 
10.8%
S 238
 
7.7%
A 234
 
7.6%
R 185
 
6.0%
. 165
 
5.4%
E 143
 
4.6%
L 138
 
4.5%
O 134
 
4.4%
I 128
 
4.2%
N 95
 
3.1%
Other values (56) 1288
41.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3080
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
332
 
10.8%
S 238
 
7.7%
A 234
 
7.6%
R 185
 
6.0%
. 165
 
5.4%
E 143
 
4.6%
L 138
 
4.5%
O 134
 
4.4%
I 128
 
4.2%
N 95
 
3.1%
Other values (56) 1288
41.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3080
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
332
 
10.8%
S 238
 
7.7%
A 234
 
7.6%
R 185
 
6.0%
. 165
 
5.4%
E 143
 
4.6%
L 138
 
4.5%
O 134
 
4.4%
I 128
 
4.2%
N 95
 
3.1%
Other values (56) 1288
41.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3080
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
332
 
10.8%
S 238
 
7.7%
A 234
 
7.6%
R 185
 
6.0%
. 165
 
5.4%
E 143
 
4.6%
L 138
 
4.5%
O 134
 
4.4%
I 128
 
4.2%
N 95
 
3.1%
Other values (56) 1288
41.8%
Distinct123
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
Minimum2016-01-11 00:00:00
Maximum2022-09-05 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-08T11:19:41.511071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:41.606135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Estado
Categorical

Distinct5
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
Inscripto
112 
Desactualizado Por Documentos Vencidos
25 
Desactualizado Por Mantencion Formulario
 
7
Pre Inscripto
 
5
En Evaluacion
 
1

Length

Max length40
Median length9
Mean length15.44
Min length9

Characters and Unicode

Total characters2316
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st rowInscripto
2nd rowDesactualizado Por Documentos Vencidos
3rd rowDesactualizado Por Documentos Vencidos
4th rowInscripto
5th rowDesactualizado Por Documentos Vencidos

Common Values

ValueCountFrequency (%)
Inscripto 112
74.7%
Desactualizado Por Documentos Vencidos 25
 
16.7%
Desactualizado Por Mantencion Formulario 7
 
4.7%
Pre Inscripto 5
 
3.3%
En Evaluacion 1
 
0.7%

Length

2025-07-08T11:19:41.699941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:41.746814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
inscripto 117
46.4%
desactualizado 32
 
12.7%
por 32
 
12.7%
documentos 25
 
9.9%
vencidos 25
 
9.9%
mantencion 7
 
2.8%
formulario 7
 
2.8%
pre 5
 
2.0%
en 1
 
0.4%
evaluacion 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
o 278
12.0%
c 207
 
8.9%
s 199
 
8.6%
n 190
 
8.2%
i 189
 
8.2%
t 181
 
7.8%
r 168
 
7.3%
I 117
 
5.1%
p 117
 
5.1%
a 112
 
4.8%
Other values (14) 558
24.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2316
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 278
12.0%
c 207
 
8.9%
s 199
 
8.6%
n 190
 
8.2%
i 189
 
8.2%
t 181
 
7.8%
r 168
 
7.3%
I 117
 
5.1%
p 117
 
5.1%
a 112
 
4.8%
Other values (14) 558
24.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2316
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 278
12.0%
c 207
 
8.9%
s 199
 
8.6%
n 190
 
8.2%
i 189
 
8.2%
t 181
 
7.8%
r 168
 
7.3%
I 117
 
5.1%
p 117
 
5.1%
a 112
 
4.8%
Other values (14) 558
24.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2316
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 278
12.0%
c 207
 
8.9%
s 199
 
8.6%
n 190
 
8.2%
i 189
 
8.2%
t 181
 
7.8%
r 168
 
7.3%
I 117
 
5.1%
p 117
 
5.1%
a 112
 
4.8%
Other values (14) 558
24.1%

TipoSocietario
Categorical

Distinct8
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
Sociedad Responsabilidad Limitada
63 
Sociedad Anónima
57 
Otras Formas Societarias
10 
Cooperativas
 
5
Persona Física
 
5
Other values (3)
10 

Length

Max length40
Median length33
Mean length24.053333
Min length12

Characters and Unicode

Total characters3608
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSociedad Anónima
2nd rowSociedades De Hecho
3rd rowOtras Formas Societarias
4th rowSociedad Anónima
5th rowSociedad Responsabilidad Limitada

Common Values

ValueCountFrequency (%)
Sociedad Responsabilidad Limitada 63
42.0%
Sociedad Anónima 57
38.0%
Otras Formas Societarias 10
 
6.7%
Cooperativas 5
 
3.3%
Persona Física 5
 
3.3%
Sociedades De Hecho 4
 
2.7%
Organismo Publico 3
 
2.0%
Persona Jurídica Extranjero Sin Sucursal 3
 
2.0%

Length

2025-07-08T11:19:41.831204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:41.894302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sociedad 120
31.5%
responsabilidad 63
16.5%
limitada 63
16.5%
anónima 57
15.0%
otras 10
 
2.6%
formas 10
 
2.6%
societarias 10
 
2.6%
persona 8
 
2.1%
cooperativas 5
 
1.3%
física 5
 
1.3%
Other values (9) 30
 
7.9%

Most occurring characters

ValueCountFrequency (%)
a 508
14.1%
i 475
13.2%
d 440
12.2%
o 238
 
6.6%
231
 
6.4%
e 225
 
6.2%
n 194
 
5.4%
s 184
 
5.1%
c 152
 
4.2%
S 140
 
3.9%
Other values (25) 821
22.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 508
14.1%
i 475
13.2%
d 440
12.2%
o 238
 
6.6%
231
 
6.4%
e 225
 
6.2%
n 194
 
5.4%
s 184
 
5.1%
c 152
 
4.2%
S 140
 
3.9%
Other values (25) 821
22.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 508
14.1%
i 475
13.2%
d 440
12.2%
o 238
 
6.6%
231
 
6.4%
e 225
 
6.2%
n 194
 
5.4%
s 184
 
5.1%
c 152
 
4.2%
S 140
 
3.9%
Other values (25) 821
22.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 508
14.1%
i 475
13.2%
d 440
12.2%
o 238
 
6.6%
231
 
6.4%
e 225
 
6.2%
n 194
 
5.4%
s 184
 
5.1%
c 152
 
4.2%
S 140
 
3.9%
Other values (25) 821
22.8%

periodo_preinscripcion
Real number (ℝ)

High correlation 

Distinct42
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean201726.93
Minimum201607
Maximum202209
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2025-07-08T11:19:41.988048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum201607
5-th percentile201610
Q1201612
median201703
Q3201708
95-th percentile202064.35
Maximum202209
Range602
Interquartile range (IQR)96

Descriptive statistics

Standard deviation135.43178
Coefficient of variation (CV)0.00067136192
Kurtosis4.4986676
Mean201726.93
Median Absolute Deviation (MAD)7
Skewness2.1168591
Sum30259040
Variance18341.767
MonotonicityNot monotonic
2025-07-08T11:19:42.112068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
201611 24
16.0%
201704 17
 
11.3%
201701 15
 
10.0%
201702 13
 
8.7%
201703 9
 
6.0%
201705 7
 
4.7%
201612 7
 
4.7%
201610 6
 
4.0%
201706 4
 
2.7%
201708 4
 
2.7%
Other values (32) 44
29.3%
ValueCountFrequency (%)
201607 2
 
1.3%
201609 2
 
1.3%
201610 6
 
4.0%
201611 24
16.0%
201612 7
 
4.7%
201701 15
10.0%
201702 13
8.7%
201703 9
 
6.0%
201704 17
11.3%
201705 7
 
4.7%
ValueCountFrequency (%)
202209 1
0.7%
202206 1
0.7%
202205 1
0.7%
202204 1
0.7%
202203 1
0.7%
202110 1
0.7%
202109 1
0.7%
202108 1
0.7%
202011 1
0.7%
202008 1
0.7%

anio_preinscripcion
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2017.2067
Minimum2016
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2025-07-08T11:19:42.186042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2016
Q12016
median2017
Q32017
95-th percentile2020.55
Maximum2022
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3624724
Coefficient of variation (CV)0.0006754253
Kurtosis4.3357231
Mean2017.2067
Median Absolute Deviation (MAD)0
Skewness2.0552298
Sum302581
Variance1.8563311
MonotonicityNot monotonic
2025-07-08T11:19:42.246124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2017 79
52.7%
2016 41
27.3%
2018 13
 
8.7%
2019 5
 
3.3%
2022 5
 
3.3%
2020 4
 
2.7%
2021 3
 
2.0%
ValueCountFrequency (%)
2016 41
27.3%
2017 79
52.7%
2018 13
 
8.7%
2019 5
 
3.3%
2020 4
 
2.7%
2021 3
 
2.0%
2022 5
 
3.3%
ValueCountFrequency (%)
2022 5
 
3.3%
2021 3
 
2.0%
2020 4
 
2.7%
2019 5
 
3.3%
2018 13
 
8.7%
2017 79
52.7%
2016 41
27.3%

cant_procesos_adjudicado
Real number (ℝ)

High correlation 

Distinct31
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.793333
Minimum1
Maximum590
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2025-07-08T11:19:42.316406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q38
95-th percentile40.75
Maximum590
Range589
Interquartile range (IQR)7

Descriptive statistics

Standard deviation51.961821
Coefficient of variation (CV)4.0616327
Kurtosis104.80957
Mean12.793333
Median Absolute Deviation (MAD)1
Skewness9.7313712
Sum1919
Variance2700.0308
MonotonicityNot monotonic
2025-07-08T11:19:42.396130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 55
36.7%
2 22
 
14.7%
3 12
 
8.0%
6 9
 
6.0%
4 7
 
4.7%
5 5
 
3.3%
12 4
 
2.7%
8 3
 
2.0%
9 3
 
2.0%
14 3
 
2.0%
Other values (21) 27
18.0%
ValueCountFrequency (%)
1 55
36.7%
2 22
 
14.7%
3 12
 
8.0%
4 7
 
4.7%
5 5
 
3.3%
6 9
 
6.0%
7 1
 
0.7%
8 3
 
2.0%
9 3
 
2.0%
10 2
 
1.3%
ValueCountFrequency (%)
590 1
0.7%
209 1
0.7%
91 1
0.7%
78 1
0.7%
57 1
0.7%
51 1
0.7%
45 1
0.7%
43 1
0.7%
38 1
0.7%
35 1
0.7%

monto_total_adjudicado
Real number (ℝ)

High correlation  Zeros 

Distinct149
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55233400
Minimum0
Maximum1.9100989 × 109
Zeros2
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2025-07-08T11:19:42.485307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile44804.898
Q1511112.94
median4552040
Q325242793
95-th percentile1.6828964 × 108
Maximum1.9100989 × 109
Range1.9100989 × 109
Interquartile range (IQR)24731680

Descriptive statistics

Standard deviation2.0925571 × 108
Coefficient of variation (CV)3.7885719
Kurtosis54.008644
Mean55233400
Median Absolute Deviation (MAD)4422417.6
Skewness7.002846
Sum8.2850099 × 109
Variance4.3787951 × 1016
MonotonicityNot monotonic
2025-07-08T11:19:42.591607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2
 
1.3%
139515.1613 1
 
0.7%
897332.6613 1
 
0.7%
13718355.26 1
 
0.7%
47514199 1
 
0.7%
43707 1
 
0.7%
129531720.6 1
 
0.7%
4155352.146 1
 
0.7%
9995957.246 1
 
0.7%
2890087.179 1
 
0.7%
Other values (139) 139
92.7%
ValueCountFrequency (%)
0 2
1.3%
0.01 1
0.7%
1912.5 1
0.7%
16790.76923 1
0.7%
29527.62 1
0.7%
36000 1
0.7%
43707 1
0.7%
46146.77419 1
0.7%
53580 1
0.7%
67961.6129 1
0.7%
ValueCountFrequency (%)
1910098932 1
0.7%
1418191729 1
0.7%
825396407.4 1
0.7%
341587058.6 1
0.7%
335474338.9 1
0.7%
321925562.7 1
0.7%
255189952.4 1
0.7%
176746834.4 1
0.7%
157953061 1
0.7%
153227947.9 1
0.7%

antiguedad
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8266667
Minimum0
Maximum5
Zeros8
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2025-07-08T11:19:42.668041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.45
Q14
median4
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2518264
Coefficient of variation (CV)0.32713234
Kurtosis2.9430821
Mean3.8266667
Median Absolute Deviation (MAD)0
Skewness-1.767717
Sum574
Variance1.5670694
MonotonicityNot monotonic
2025-07-08T11:19:42.721477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 79
52.7%
5 41
27.3%
3 13
 
8.7%
0 8
 
5.3%
2 5
 
3.3%
1 4
 
2.7%
ValueCountFrequency (%)
0 8
 
5.3%
1 4
 
2.7%
2 5
 
3.3%
3 13
 
8.7%
4 79
52.7%
5 41
27.3%
ValueCountFrequency (%)
5 41
27.3%
4 79
52.7%
3 13
 
8.7%
2 5
 
3.3%
1 4
 
2.7%
0 8
 
5.3%

provincia
Categorical

High correlation 

Distinct19
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Memory size17.7 KiB
Ciudad Autónoma de Buenos Aires
82 
Buenos Aires
25 
Córdoba
13 
Santa Fe
 
6
San Luis
 
4
Other values (14)
20 

Length

Max length31
Median length31
Mean length21.246667
Min length5

Characters and Unicode

Total characters3187
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)6.7%

Sample

1st rowCiudad Autónoma de Buenos Aires
2nd rowBuenos Aires
3rd rowBuenos Aires
4th rowCiudad Autónoma de Buenos Aires
5th rowCiudad Autónoma de Buenos Aires

Common Values

ValueCountFrequency (%)
Ciudad Autónoma de Buenos Aires 82
54.7%
Buenos Aires 25
 
16.7%
Córdoba 13
 
8.7%
Santa Fe 6
 
4.0%
San Luis 4
 
2.7%
Extranjera 3
 
2.0%
Rio Negro 3
 
2.0%
La Pampa 2
 
1.3%
Tucumán 2
 
1.3%
Santa Cruz 1
 
0.7%
Other values (9) 9
 
6.0%

Length

2025-07-08T11:19:42.786326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aires 107
20.5%
buenos 107
20.5%
ciudad 82
15.7%
de 82
15.7%
autónoma 82
15.7%
córdoba 13
 
2.5%
santa 7
 
1.3%
fe 6
 
1.1%
san 5
 
1.0%
luis 4
 
0.8%
Other values (19) 28
 
5.4%

Most occurring characters

ValueCountFrequency (%)
373
11.7%
e 317
9.9%
u 286
9.0%
d 261
 
8.2%
s 222
 
7.0%
o 214
 
6.7%
a 212
 
6.7%
n 212
 
6.7%
i 201
 
6.3%
A 189
 
5.9%
Other values (28) 700
22.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3187
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
373
11.7%
e 317
9.9%
u 286
9.0%
d 261
 
8.2%
s 222
 
7.0%
o 214
 
6.7%
a 212
 
6.7%
n 212
 
6.7%
i 201
 
6.3%
A 189
 
5.9%
Other values (28) 700
22.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3187
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
373
11.7%
e 317
9.9%
u 286
9.0%
d 261
 
8.2%
s 222
 
7.0%
o 214
 
6.7%
a 212
 
6.7%
n 212
 
6.7%
i 201
 
6.3%
A 189
 
5.9%
Other values (28) 700
22.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3187
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
373
11.7%
e 317
9.9%
u 286
9.0%
d 261
 
8.2%
s 222
 
7.0%
o 214
 
6.7%
a 212
 
6.7%
n 212
 
6.7%
i 201
 
6.3%
A 189
 
5.9%
Other values (28) 700
22.0%

cant_socios
Real number (ℝ)

Zeros 

Distinct12
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4733333
Minimum0
Maximum31
Zeros8
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2025-07-08T11:19:42.832304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.45
Q11
median2
Q33
95-th percentile5.55
Maximum31
Range31
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.4751209
Coefficient of variation (CV)1.4050354
Kurtosis38.875019
Mean2.4733333
Median Absolute Deviation (MAD)1
Skewness5.7302229
Sum371
Variance12.076465
MonotonicityNot monotonic
2025-07-08T11:19:42.896993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 54
36.0%
1 48
32.0%
3 25
16.7%
0 8
 
5.3%
4 5
 
3.3%
6 3
 
2.0%
5 2
 
1.3%
31 1
 
0.7%
10 1
 
0.7%
18 1
 
0.7%
Other values (2) 2
 
1.3%
ValueCountFrequency (%)
0 8
 
5.3%
1 48
32.0%
2 54
36.0%
3 25
16.7%
4 5
 
3.3%
5 2
 
1.3%
6 3
 
2.0%
10 1
 
0.7%
11 1
 
0.7%
18 1
 
0.7%
ValueCountFrequency (%)
31 1
 
0.7%
22 1
 
0.7%
18 1
 
0.7%
11 1
 
0.7%
10 1
 
0.7%
6 3
 
2.0%
5 2
 
1.3%
4 5
 
3.3%
3 25
16.7%
2 54
36.0%

cant_apercibimientos
Categorical

Constant 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
0.0
150 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters450
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 150
100.0%

Length

2025-07-08T11:19:42.996502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:43.043338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 150
100.0%

Most occurring characters

ValueCountFrequency (%)
0 300
66.7%
. 150
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 300
66.7%
. 150
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 300
66.7%
. 150
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 300
66.7%
. 150
33.3%

cant_suspensiones
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
0.0
149 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters450
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 149
99.3%
1.0 1
 
0.7%

Length

2025-07-08T11:19:43.100075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:43.145991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 149
99.3%
1.0 1
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 299
66.4%
. 150
33.3%
1 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 299
66.4%
. 150
33.3%
1 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 299
66.4%
. 150
33.3%
1 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 299
66.4%
. 150
33.3%
1 1
 
0.2%

cant_antecedentes
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
0.0
149 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters450
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 149
99.3%
1.0 1
 
0.7%

Length

2025-07-08T11:19:43.203810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:43.249645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 149
99.3%
1.0 1
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 299
66.4%
. 150
33.3%
1 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 299
66.4%
. 150
33.3%
1 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 299
66.4%
. 150
33.3%
1 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 299
66.4%
. 150
33.3%
1 1
 
0.2%

cant_Apoderado
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.18
Minimum0
Maximum6
Zeros41
Zeros (%)27.3%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2025-07-08T11:19:43.289484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0934215
Coefficient of variation (CV)0.92662835
Kurtosis2.790168
Mean1.18
Median Absolute Deviation (MAD)1
Skewness1.3524968
Sum177
Variance1.1955705
MonotonicityNot monotonic
2025-07-08T11:19:43.349890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 65
43.3%
0 41
27.3%
2 29
19.3%
3 9
 
6.0%
4 4
 
2.7%
6 1
 
0.7%
5 1
 
0.7%
ValueCountFrequency (%)
0 41
27.3%
1 65
43.3%
2 29
19.3%
3 9
 
6.0%
4 4
 
2.7%
5 1
 
0.7%
6 1
 
0.7%
ValueCountFrequency (%)
6 1
 
0.7%
5 1
 
0.7%
4 4
 
2.7%
3 9
 
6.0%
2 29
19.3%
1 65
43.3%
0 41
27.3%

cant_representante
Categorical

High correlation 

Distinct5
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
1.0
75 
0.0
62 
2.0
11 
4.0
 
1
3.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters450
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.3%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 75
50.0%
0.0 62
41.3%
2.0 11
 
7.3%
4.0 1
 
0.7%
3.0 1
 
0.7%

Length

2025-07-08T11:19:43.422647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:43.475400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 75
50.0%
0.0 62
41.3%
2.0 11
 
7.3%
4.0 1
 
0.7%
3.0 1
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 212
47.1%
. 150
33.3%
1 75
 
16.7%
2 11
 
2.4%
4 1
 
0.2%
3 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 212
47.1%
. 150
33.3%
1 75
 
16.7%
2 11
 
2.4%
4 1
 
0.2%
3 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 212
47.1%
. 150
33.3%
1 75
 
16.7%
2 11
 
2.4%
4 1
 
0.2%
3 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 212
47.1%
. 150
33.3%
1 75
 
16.7%
2 11
 
2.4%
4 1
 
0.2%
3 1
 
0.2%

cant_autenticado
Categorical

High correlation 

Distinct5
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
1.0
102 
2.0
36 
3.0
 
10
6.0
 
1
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters450
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.3%

Sample

1st row1.0
2nd row1.0
3rd row6.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 102
68.0%
2.0 36
 
24.0%
3.0 10
 
6.7%
6.0 1
 
0.7%
4.0 1
 
0.7%

Length

2025-07-08T11:19:43.547876image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:43.603436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 102
68.0%
2.0 36
 
24.0%
3.0 10
 
6.7%
6.0 1
 
0.7%
4.0 1
 
0.7%

Most occurring characters

ValueCountFrequency (%)
. 150
33.3%
0 150
33.3%
1 102
22.7%
2 36
 
8.0%
3 10
 
2.2%
6 1
 
0.2%
4 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 150
33.3%
0 150
33.3%
1 102
22.7%
2 36
 
8.0%
3 10
 
2.2%
6 1
 
0.2%
4 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 150
33.3%
0 150
33.3%
1 102
22.7%
2 36
 
8.0%
3 10
 
2.2%
6 1
 
0.2%
4 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 150
33.3%
0 150
33.3%
1 102
22.7%
2 36
 
8.0%
3 10
 
2.2%
6 1
 
0.2%
4 1
 
0.2%

cant_noAutenticado
Categorical

High correlation 

Distinct5
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
0.0
102 
1.0
35 
2.0
 
9
4.0
 
2
3.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters450
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 102
68.0%
1.0 35
 
23.3%
2.0 9
 
6.0%
4.0 2
 
1.3%
3.0 2
 
1.3%

Length

2025-07-08T11:19:43.675266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:43.731140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 102
68.0%
1.0 35
 
23.3%
2.0 9
 
6.0%
4.0 2
 
1.3%
3.0 2
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 252
56.0%
. 150
33.3%
1 35
 
7.8%
2 9
 
2.0%
4 2
 
0.4%
3 2
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 252
56.0%
. 150
33.3%
1 35
 
7.8%
2 9
 
2.0%
4 2
 
0.4%
3 2
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 252
56.0%
. 150
33.3%
1 35
 
7.8%
2 9
 
2.0%
4 2
 
0.4%
3 2
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 252
56.0%
. 150
33.3%
1 35
 
7.8%
2 9
 
2.0%
4 2
 
0.4%
3 2
 
0.4%

cant_sinMontoLimite
Categorical

High correlation 

Distinct5
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
0.0
85 
1.0
46 
2.0
14 
3.0
 
4
6.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters450
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 85
56.7%
1.0 46
30.7%
2.0 14
 
9.3%
3.0 4
 
2.7%
6.0 1
 
0.7%

Length

2025-07-08T11:19:43.806799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:43.864406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 85
56.7%
1.0 46
30.7%
2.0 14
 
9.3%
3.0 4
 
2.7%
6.0 1
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 235
52.2%
. 150
33.3%
1 46
 
10.2%
2 14
 
3.1%
3 4
 
0.9%
6 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 235
52.2%
. 150
33.3%
1 46
 
10.2%
2 14
 
3.1%
3 4
 
0.9%
6 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 235
52.2%
. 150
33.3%
1 46
 
10.2%
2 14
 
3.1%
3 4
 
0.9%
6 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 235
52.2%
. 150
33.3%
1 46
 
10.2%
2 14
 
3.1%
3 4
 
0.9%
6 1
 
0.2%

cant_MontoLimite
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size10.0 KiB
1.0
119 
2.0
26 
3.0
 
3
6.0
 
1
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters450
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.3%

Sample

1st row2.0
2nd row1.0
3rd row6.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 119
79.3%
2.0 26
 
17.3%
3.0 3
 
2.0%
6.0 1
 
0.7%
4.0 1
 
0.7%

Length

2025-07-08T11:19:43.939159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:43.994885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 119
79.3%
2.0 26
 
17.3%
3.0 3
 
2.0%
6.0 1
 
0.7%
4.0 1
 
0.7%

Most occurring characters

ValueCountFrequency (%)
. 150
33.3%
0 150
33.3%
1 119
26.4%
2 26
 
5.8%
3 3
 
0.7%
6 1
 
0.2%
4 1
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 150
33.3%
0 150
33.3%
1 119
26.4%
2 26
 
5.8%
3 3
 
0.7%
6 1
 
0.2%
4 1
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 150
33.3%
0 150
33.3%
1 119
26.4%
2 26
 
5.8%
3 3
 
0.7%
6 1
 
0.2%
4 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 150
33.3%
0 150
33.3%
1 119
26.4%
2 26
 
5.8%
3 3
 
0.7%
6 1
 
0.2%
4 1
 
0.2%

total_articulos_provee
Real number (ℝ)

High correlation 

Distinct66
Distinct (%)44.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.106667
Minimum1
Maximum755
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2025-07-08T11:19:44.071701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median12
Q340
95-th percentile208.2
Maximum755
Range754
Interquartile range (IQR)36

Descriptive statistics

Standard deviation115.1385
Coefficient of variation (CV)2.1680612
Kurtosis19.691315
Mean53.106667
Median Absolute Deviation (MAD)11
Skewness4.1684298
Sum7966
Variance13256.874
MonotonicityNot monotonic
2025-07-08T11:19:44.171369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 20
 
13.3%
3 12
 
8.0%
8 6
 
4.0%
7 6
 
4.0%
12 6
 
4.0%
6 5
 
3.3%
2 5
 
3.3%
9 5
 
3.3%
13 4
 
2.7%
5 4
 
2.7%
Other values (56) 77
51.3%
ValueCountFrequency (%)
1 20
13.3%
2 5
 
3.3%
3 12
8.0%
4 4
 
2.7%
5 4
 
2.7%
6 5
 
3.3%
7 6
 
4.0%
8 6
 
4.0%
9 5
 
3.3%
10 2
 
1.3%
ValueCountFrequency (%)
755 1
0.7%
703 1
0.7%
619 1
0.7%
480 1
0.7%
331 1
0.7%
327 1
0.7%
243 1
0.7%
219 1
0.7%
195 1
0.7%
163 1
0.7%
Distinct20
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size13.2 KiB
(377939.298, 599760.0]
13 
(13557176.81, 19975532.58]
11 
(6702697.888, 9424898.401]
11 
(46718747.516, 89439449.702]
10 
(89439449.702, 222964579.98]
10 
Other values (15)
95 

Length

Max length29
Median length28
Mean length24.986667
Min length19

Characters and Unicode

Total characters3748
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(46718747.516, 89439449.702]
2nd row(104767.373, 224078.198]
3rd row(890758.9, 1302657.558]
4th row(13557176.81, 19975532.58]
5th row(33011.111, 104767.373]

Common Values

ValueCountFrequency (%)
(377939.298, 599760.0] 13
 
8.7%
(13557176.81, 19975532.58] 11
 
7.3%
(6702697.888, 9424898.401] 11
 
7.3%
(46718747.516, 89439449.702] 10
 
6.7%
(89439449.702, 222964579.98] 10
 
6.7%
(3396600.0, 4727330.113] 9
 
6.0%
(224078.198, 377939.298] 8
 
5.3%
(1793326.755, 2483085.385] 8
 
5.3%
(33011.111, 104767.373] 8
 
5.3%
(9424898.401, 13557176.81] 8
 
5.3%
Other values (10) 54
36.0%

Length

2025-07-08T11:19:44.287111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
377939.298 21
 
7.0%
89439449.702 20
 
6.7%
13557176.81 19
 
6.3%
9424898.401 19
 
6.3%
599760.0 18
 
6.0%
46718747.516 17
 
5.7%
222964579.98 17
 
5.7%
6702697.888 16
 
5.3%
19975532.58 16
 
5.3%
3396600.0 16
 
5.3%
Other values (11) 121
40.3%

Most occurring characters

ValueCountFrequency (%)
9 366
9.8%
7 360
9.6%
1 338
9.0%
. 300
 
8.0%
3 295
 
7.9%
8 293
 
7.8%
0 269
 
7.2%
5 253
 
6.8%
4 244
 
6.5%
2 223
 
5.9%
Other values (6) 807
21.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3748
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 366
9.8%
7 360
9.6%
1 338
9.0%
. 300
 
8.0%
3 295
 
7.9%
8 293
 
7.8%
0 269
 
7.2%
5 253
 
6.8%
4 244
 
6.5%
2 223
 
5.9%
Other values (6) 807
21.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3748
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 366
9.8%
7 360
9.6%
1 338
9.0%
. 300
 
8.0%
3 295
 
7.9%
8 293
 
7.8%
0 269
 
7.2%
5 253
 
6.8%
4 244
 
6.5%
2 223
 
5.9%
Other values (6) 807
21.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3748
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 366
9.8%
7 360
9.6%
1 338
9.0%
. 300
 
8.0%
3 295
 
7.9%
8 293
 
7.8%
0 269
 
7.2%
5 253
 
6.8%
4 244
 
6.5%
2 223
 
5.9%
Other values (6) 807
21.5%
Distinct10
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
(0.999, 2.0]
77 
(2.0, 3.0]
12 
(8.0, 12.0]
11 
(19.0, 39.0]
10 
(5.0, 6.0]
Other values (5)
31 

Length

Max length14
Median length12
Mean length11.54
Min length10

Characters and Unicode

Total characters1731
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(2.0, 3.0]
2nd row(0.999, 2.0]
3rd row(0.999, 2.0]
4th row(19.0, 39.0]
5th row(0.999, 2.0]

Common Values

ValueCountFrequency (%)
(0.999, 2.0] 77
51.3%
(2.0, 3.0] 12
 
8.0%
(8.0, 12.0] 11
 
7.3%
(19.0, 39.0] 10
 
6.7%
(5.0, 6.0] 9
 
6.0%
(39.0, 1214.0] 8
 
5.3%
(3.0, 4.0] 7
 
4.7%
(12.0, 19.0] 7
 
4.7%
(4.0, 5.0] 5
 
3.3%
(6.0, 8.0] 4
 
2.7%

Length

2025-07-08T11:19:44.594858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:44.671443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 89
29.7%
0.999 77
25.7%
3.0 19
 
6.3%
12.0 18
 
6.0%
39.0 18
 
6.0%
19.0 17
 
5.7%
8.0 15
 
5.0%
5.0 14
 
4.7%
6.0 13
 
4.3%
4.0 12
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 300
17.3%
. 300
17.3%
9 266
15.4%
( 150
8.7%
, 150
8.7%
150
8.7%
] 150
8.7%
2 115
 
6.6%
1 51
 
2.9%
3 37
 
2.1%
Other values (4) 62
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1731
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 300
17.3%
. 300
17.3%
9 266
15.4%
( 150
8.7%
, 150
8.7%
150
8.7%
] 150
8.7%
2 115
 
6.6%
1 51
 
2.9%
3 37
 
2.1%
Other values (4) 62
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1731
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 300
17.3%
. 300
17.3%
9 266
15.4%
( 150
8.7%
, 150
8.7%
150
8.7%
] 150
8.7%
2 115
 
6.6%
1 51
 
2.9%
3 37
 
2.1%
Other values (4) 62
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1731
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 300
17.3%
. 300
17.3%
9 266
15.4%
( 150
8.7%
, 150
8.7%
150
8.7%
] 150
8.7%
2 115
 
6.6%
1 51
 
2.9%
3 37
 
2.1%
Other values (4) 62
 
3.6%

dtotal_articulos_provee
Categorical

High correlation 

Distinct15
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
(0.999, 2.0]
25 
(11.0, 15.0]
15 
(97.6, 161.0]
14 
(6.0, 8.0]
12 
(2.0, 3.0]
12 
Other values (10)
72 

Length

Max length15
Median length12
Mean length11.693333
Min length10

Characters and Unicode

Total characters1754
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(0.999, 2.0]
2nd row(0.999, 2.0]
3rd row(4.0, 6.0]
4th row(58.0, 97.6]
5th row(0.999, 2.0]

Common Values

ValueCountFrequency (%)
(0.999, 2.0] 25
16.7%
(11.0, 15.0] 15
10.0%
(97.6, 161.0] 14
9.3%
(6.0, 8.0] 12
8.0%
(2.0, 3.0] 12
8.0%
(29.0, 40.0] 12
8.0%
(8.0, 11.0] 10
 
6.7%
(4.0, 6.0] 9
 
6.0%
(58.0, 97.6] 8
 
5.3%
(21.0, 29.0] 8
 
5.3%
Other values (5) 25
16.7%

Length

2025-07-08T11:19:44.760001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2.0 37
12.3%
0.999 25
 
8.3%
11.0 25
 
8.3%
97.6 22
 
7.3%
8.0 22
 
7.3%
15.0 21
 
7.0%
6.0 21
 
7.0%
161.0 20
 
6.7%
29.0 20
 
6.7%
40.0 17
 
5.7%
Other values (6) 70
23.3%

Most occurring characters

ValueCountFrequency (%)
. 300
17.1%
0 295
16.8%
( 150
8.6%
, 150
8.6%
150
8.6%
] 150
8.6%
9 125
7.1%
1 125
7.1%
2 71
 
4.0%
6 67
 
3.8%
Other values (5) 171
9.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1754
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 300
17.1%
0 295
16.8%
( 150
8.6%
, 150
8.6%
150
8.6%
] 150
8.6%
9 125
7.1%
1 125
7.1%
2 71
 
4.0%
6 67
 
3.8%
Other values (5) 171
9.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1754
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 300
17.1%
0 295
16.8%
( 150
8.6%
, 150
8.6%
150
8.6%
] 150
8.6%
9 125
7.1%
1 125
7.1%
2 71
 
4.0%
6 67
 
3.8%
Other values (5) 171
9.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1754
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 300
17.1%
0 295
16.8%
( 150
8.6%
, 150
8.6%
150
8.6%
] 150
8.6%
9 125
7.1%
1 125
7.1%
2 71
 
4.0%
6 67
 
3.8%
Other values (5) 171
9.7%

cluster_k5
Categorical

Constant 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size9.7 KiB
3
150 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters150
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 150
100.0%

Length

2025-07-08T11:19:44.833476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T11:19:44.878498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3 150
100.0%

Most occurring characters

ValueCountFrequency (%)
3 150
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 150
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 150
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 150
100.0%

Interactions

2025-07-08T11:19:39.585913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:35.546055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:36.173923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:36.720828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:37.598849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:38.143118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:38.615511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:39.110910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:39.648405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:35.608619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:36.240521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:36.790368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:37.676897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:38.206446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:38.678074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:39.173472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:39.711555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:35.671117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:36.310514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:36.893993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:37.739469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:38.269032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:38.740569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:39.220855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:39.784299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:35.749152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:36.379239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:36.964206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:37.812863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:38.317609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:38.805688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:39.283432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:39.855891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:35.867084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:36.453850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:37.363336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:37.877522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:38.395739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:38.860934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:39.350215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:39.917653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:35.949667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:36.515434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:37.410279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:37.940033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:38.442608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:38.923498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:39.412634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:39.985085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:36.023994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:36.581545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:37.472773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:38.002517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:38.505953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:38.986004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:39.459582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:40.203922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:36.098534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:36.652905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:37.536277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:38.080625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:38.553088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:39.048488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T11:19:39.522784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-08T11:19:44.935163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
EstadoTipoSocietarioanio_preinscripcionantiguedadcant_Apoderadocant_MontoLimitecant_antecedentescant_autenticadocant_noAutenticadocant_procesos_adjudicadocant_representantecant_sinMontoLimitecant_socioscant_suspensionesdcant_procesos_adjudicadodmonto_total_adjudicadodtotal_articulos_proveemonto_total_adjudicadoperiodo_preinscripcionprovinciatotal_articulos_provee
Estado1.0000.2600.1350.1180.0000.0000.0000.0000.0000.0000.0670.0000.0000.0000.0000.0640.0210.0000.1350.0000.000
TipoSocietario0.2601.0000.1950.2120.1640.0000.2170.1740.0000.1920.0000.1680.0560.2170.0680.0000.0000.0000.1950.3810.000
anio_preinscripcion0.1350.1951.000-1.000-0.2290.0000.0000.0000.000-0.3190.0000.000-0.0700.0000.0000.0000.057-0.2190.9160.259-0.083
antiguedad0.1180.212-1.0001.0000.2290.0000.0000.0000.0000.3190.0000.0000.0720.0000.0000.0000.0120.219-0.9160.1970.083
cant_Apoderado0.0000.164-0.2290.2291.0000.7410.0000.6100.5540.1770.3360.5130.1060.0000.0000.1850.1650.258-0.2030.000-0.135
cant_MontoLimite0.0000.0000.0000.0000.7411.0000.0720.5270.4060.0000.0940.0000.4840.0720.0000.1910.1390.1240.0000.0000.000
cant_antecedentes0.0000.2170.0000.0000.0000.0721.0000.0000.0000.6930.0000.0000.0000.4910.2430.0000.0000.0000.0000.0000.155
cant_autenticado0.0000.1740.0000.0000.6100.5270.0001.0000.3490.0000.5040.5800.1660.0000.0560.1190.1440.0570.0000.0000.000
cant_noAutenticado0.0000.0000.0000.0000.5540.4060.0000.3491.0000.0250.4100.5040.3620.0000.0000.2620.0000.2930.0000.0000.000
cant_procesos_adjudicado0.0000.192-0.3190.3190.1770.0000.6930.0000.0251.0000.0000.0990.0260.6930.3220.0000.1700.624-0.3540.0000.311
cant_representante0.0670.0000.0000.0000.3360.0940.0000.5040.4100.0001.0000.5010.0520.0000.1150.0000.0000.4870.0000.0000.000
cant_sinMontoLimite0.0000.1680.0000.0000.5130.0000.0000.5800.5040.0990.5011.0000.1410.0000.1700.1420.2570.1580.0000.0000.000
cant_socios0.0000.056-0.0700.0720.1060.4840.0000.1660.3620.0260.0520.1411.0000.0000.1050.1630.1750.103-0.0940.000-0.075
cant_suspensiones0.0000.2170.0000.0000.0000.0720.4910.0000.0000.6930.0000.0000.0001.0000.2430.0000.0000.0000.0000.0000.155
dcant_procesos_adjudicado0.0000.0680.0000.0000.0000.0000.2430.0560.0000.3220.1150.1700.1050.2431.0000.2060.1150.2320.0000.0750.218
dmonto_total_adjudicado0.0640.0000.0000.0000.1850.1910.0000.1190.2620.0000.0000.1420.1630.0000.2061.0000.1590.3550.0000.1800.000
dtotal_articulos_provee0.0210.0000.0570.0120.1650.1390.0000.1440.0000.1700.0000.2570.1750.0000.1150.1591.0000.0000.0570.1670.525
monto_total_adjudicado0.0000.000-0.2190.2190.2580.1240.0000.0570.2930.6240.4870.1580.1030.0000.2320.3550.0001.000-0.2650.0000.181
periodo_preinscripcion0.1350.1950.916-0.916-0.2030.0000.0000.0000.000-0.3540.0000.000-0.0940.0000.0000.0000.057-0.2651.0000.259-0.048
provincia0.0000.3810.2590.1970.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0750.1800.1670.0000.2591.0000.577
total_articulos_provee0.0000.000-0.0830.083-0.1350.0000.1550.0000.0000.3110.0000.000-0.0750.1550.2180.0000.5250.181-0.0480.5771.000

Missing values

2025-07-08T11:19:40.362324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-08T11:19:40.559202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CUITNombreFechaPreinscripcionEstadoTipoSocietarioperiodo_preinscripcionanio_preinscripcioncant_procesos_adjudicadomonto_total_adjudicadoantiguedadprovinciacant_socioscant_apercibimientoscant_suspensionescant_antecedentescant_Apoderadocant_representantecant_autenticadocant_noAutenticadocant_sinMontoLimitecant_MontoLimitetotal_articulos_proveedmonto_total_adjudicadodcant_procesos_adjudicadodtotal_articulos_proveecluster_k5
20130558766987EDITORIAL ALBATROS15/11/2016InscriptoSociedad Anónima20161120163.04.751420e+075.0Ciudad Autónoma de Buenos Aires3.00.00.00.01.01.01.01.00.02.01.0(46718747.516, 89439449.702](2.0, 3.0](0.999, 2.0]3
20430715168800construir futuros26/07/2016Desactualizado Por Documentos VencidosSociedades De Hecho20160720162.01.395152e+055.0Buenos Aires1.00.00.00.01.00.01.00.00.01.01.0(104767.373, 224078.198](0.999, 2.0](0.999, 2.0]3
33530664177818FUNDACION DE LA UNIVERSIDAD NACIONAL DEL SUR13/06/2017Desactualizado Por Documentos VencidosOtras Formas Societarias20170620172.08.973327e+054.0Buenos Aires1.00.00.00.06.01.06.01.01.06.06.0(890758.9, 1302657.558](0.999, 2.0](4.0, 6.0]3
38230620477113MULTICABLE S.A.12/01/2017InscriptoSociedad Anónima201701201728.01.371836e+074.0Ciudad Autónoma de Buenos Aires1.00.00.00.01.00.01.00.00.01.070.0(13557176.81, 19975532.58](19.0, 39.0](58.0, 97.6]3
38630629086524AD-HOC S.R.L.21/06/2017Desactualizado Por Documentos VencidosSociedad Responsabilidad Limitada20170620171.04.370700e+044.0Ciudad Autónoma de Buenos Aires1.00.00.00.01.01.01.01.01.01.01.0(33011.111, 104767.373](0.999, 2.0](0.999, 2.0]3
41533708630409ITSG SRL14/11/2016InscriptoSociedad Responsabilidad Limitada201611201622.01.295317e+085.0Ciudad Autónoma de Buenos Aires1.00.00.00.01.00.01.00.00.01.0151.0(89439449.702, 222964579.98](19.0, 39.0](97.6, 161.0]3
42330525366657Refinitiv Ltd03/01/2017InscriptoOtras Formas Societarias201701201717.09.995957e+064.0Ciudad Autónoma de Buenos Aires1.00.00.00.02.00.02.00.01.01.03.0(9424898.401, 13557176.81](12.0, 19.0](2.0, 3.0]3
45830505725774FUNDARG S.R.L07/07/2017InscriptoSociedad Responsabilidad Limitada20170720174.04.155352e+064.0Córdoba2.00.00.00.01.01.02.00.00.02.07.0(3396600.0, 4727330.113](3.0, 4.0](6.0, 8.0]3
60230710910916Cooperativa de Trabajo Dario Santillan Limitada08/11/2016InscriptoCooperativas201611201617.01.532279e+085.0Ciudad Autónoma de Buenos Aires3.00.00.00.01.00.01.00.00.01.0114.0(89439449.702, 222964579.98](12.0, 19.0](97.6, 161.0]3
72330501778407SINAX S.A.16/08/2017InscriptoSociedad Anónima20170820173.02.890087e+064.0Buenos Aires4.00.00.00.04.04.04.04.06.02.03.0(2483085.385, 3396600.0](2.0, 3.0](2.0, 3.0]3
CUITNombreFechaPreinscripcionEstadoTipoSocietarioperiodo_preinscripcionanio_preinscripcioncant_procesos_adjudicadomonto_total_adjudicadoantiguedadprovinciacant_socioscant_apercibimientoscant_suspensionescant_antecedentescant_Apoderadocant_representantecant_autenticadocant_noAutenticadocant_sinMontoLimitecant_MontoLimitetotal_articulos_proveedmonto_total_adjudicadodcant_procesos_adjudicadodtotal_articulos_proveecluster_k5
882030715853465PRODUCCIONES LA MAQUINA S.R.L.18/04/2018InscriptoSociedad Responsabilidad Limitada20180420182.01.571110e+073.0Ciudad Autónoma de Buenos Aires2.00.00.00.01.01.01.01.01.01.012.0(13557176.81, 19975532.58](0.999, 2.0](11.0, 15.0]3
885330714777439ANGEL BOHORQUEZ y DEL HIERRO ROBERTO SH11/07/2019InscriptoSociedades De Hecho20190720191.06.425723e+062.0Buenos Aires2.00.00.00.02.00.02.00.00.02.011.0(4727330.113, 6702697.888](0.999, 2.0](8.0, 11.0]3
941530638733427ALDO BOLZAN Y HNOS SRL18/04/2017InscriptoSociedad Responsabilidad Limitada20170420171.06.763057e+054.0Córdoba2.00.00.00.00.02.01.01.00.02.026.0(599760.0, 890758.9](0.999, 2.0](21.0, 29.0]3
957130711460418PATAGONIA STEEL S.R.L.07/09/2021InscriptoSociedad Responsabilidad Limitada20210920211.08.137786e+050.0Chubut1.00.00.00.00.01.01.00.00.01.0219.0(599760.0, 890758.9](0.999, 2.0](161.0, 345.0]3
959130709902527CASEROS PARK S.A.24/11/2016InscriptoSociedad Anónima20161120161.02.880000e+075.0Ciudad Autónoma de Buenos Aires3.00.00.00.01.00.01.00.00.01.01.0(19975532.58, 30451916.51](0.999, 2.0](0.999, 2.0]3
969330716844583GRUPO WG S.A.S20/04/2022InscriptoOtras Formas Societarias20220420222.02.508003e+050.0Corrientes2.00.00.00.00.01.01.00.00.01.0703.0(224078.198, 377939.298](0.999, 2.0](345.0, 6993.0]3
983130716327279Diversitas S.R.L.09/05/2022InscriptoSociedad Responsabilidad Limitada20220520221.04.050000e+050.0Ciudad Autónoma de Buenos Aires2.00.00.00.00.01.01.00.00.01.024.0(377939.298, 599760.0](0.999, 2.0](21.0, 29.0]3
990730700951738MVS SA15/03/2022InscriptoSociedad Anónima20220320221.04.371429e+060.0Ciudad Autónoma de Buenos Aires2.00.00.00.02.00.01.01.00.02.04.0(3396600.0, 4727330.113](0.999, 2.0](3.0, 4.0]3
997130712369120PINTURERIA ARGENTINA SH24/06/2022InscriptoSociedades De Hecho20220620221.04.614677e+040.0Rio Negro3.00.00.00.00.01.01.00.00.01.033.0(33011.111, 104767.373](0.999, 2.0](29.0, 40.0]3
1007330700503891GIJON SA07/09/2022InscriptoSociedad Anónima20220920221.01.119596e+070.0Ciudad Autónoma de Buenos Aires3.00.00.00.01.00.01.00.00.01.01.0(9424898.401, 13557176.81](0.999, 2.0](0.999, 2.0]3